Fake Reviews Research Articles

A metadata-aware detection model for fake restaurant reviews based on multimodal fusion

A metadata-aware detection model for fake restaurant reviews based on multimodal fusion

Fake Review Detection Model Based on Comment Content and Review Behavior

With the development of the Internet, services such as catering, beauty, accommodation, and entertainment can be reserved or consumed online. Therefore, consumers increasingly rely on online information to choose merchants, products, and services, with reviews becoming a crucial factor in their decision making. However, the authenticity of reviews is highly debated in the field of Internet-based process-of-life service consumption. In recent years, due to the rapid growth of these industries, the detection of fake reviews has gained increasing attention. Fake reviews seriously mislead customers and damage the authenticity of online reviews. Various fake review classifiers have been developed, taking into account the content of the reviews and the behavior involved in the reviews, such as rating, time, etc. However, there has been no research considering the credibility of reviewers and merchants as part of identifying fake reviews. In order to improve the accuracy of existing fake review classification and detection methods, this study utilizes a comment text processing module to model the content of reviews, utilizes a reviewer behavior processing module and a reviewed merchant behavior processing module to model consumer review behavior sequences that imply reviewer credibility and merchant review behavior sequences that imply merchant credibility, respectively, and finally merges the two features for fake review classification. The experimental results show that, compared to other models, the model proposed in this paper improves the classification performance by simultaneously modeling the content of reviews and the credibility of reviewers and merchants.

Deep learning based online fake review detection technique

Deep learning based online fake review detection technique

Survey on Fake Review Detection System

Abstract: In today's business landscape, online reviews play a crucial role in shaping commerce. A large portion of purchase decisions for online products is driven by customer feedback. Consequently, some individuals or groups try to manipulate product reviews to their advantage. Fake online reviews have a considerable effect on the experiences of online consumers, sellers, and e-commerce platforms. Although there has been academic research focused on identifying fake reviews, there is still a need for studies that thoroughly examine and summarize their origins and impacts. This work introduces a combination of semi-supervised and supervised text mining models to detect fake reviews, comparing their performance using datasets from reviews.

Beyond Text: Multimodal Credibility Assessment Approaches for Online User-Generated Content

User-generated content (UGC) is increasingly becoming prevalent on various digital platforms. The content generated on social media, review forums, and question–answer platforms impacts a larger audience and influences their political, social, and other cognitive abilities. Traditional credibility assessment mechanisms involve assessing the credibility of the source and the text. However, with the increase in how user content can be generated and shared (audio, video, and images), multimodal representation of UGC has become increasingly popular. This article reviews the credibility assessment of UGC in various domains, particularly identifying fake news, suspicious profiles, and fake reviews and testimonials, focusing on both textual content and the source of the content creator. Next, the concept of multimodal credibility assessment is presented, which also includes audio, video, and images in addition to text. After that, the article presents a systematic review and comprehensive analysis of work done in the credibility assessment of UGC considering multimodal features. Additionally, the article provides extensive details on the publicly available multimodal datasets for the credibility assessment of UGC. In the end, the research gaps, challenges, and future directions in assessing the credibility of multimodal UGC are presented.

Fake Review Detection Using Machine learning and Deep Learning

The widespread adoption of Web 2.0 platforms has enabled consumers to share their opinions on products and services, influencing purchasing decisions. However, the proliferation of spam reviews has undermined the credibility of online reviews. This study aims to identify and evaluate existing approaches for detecting reviews, individual spammers, and Organizations. We categorized machine learning (ML) and deep learning (DL) techniques used for Review detection and assessed their effectiveness. Our findings indicate that accuracy is the most frequently used metric (25%), followed by recall (24%) and precision (22%). Additionally, we observed that utilizing the entire Amazon dataset can enhance the performance of F-measure, AUC, and F1-score metrics by 7%. Our study concludes that SMS spam filtering strategies are often effective in combating spam reviews. Furthermore, we developed a taxonomy of existing methodologies and observed a significant number of studies employing SMS anti-spam applications. This research uncovered innovative applications of ML and DL to spam review detection, offering a novel approach to addressing this issue. Our findings provide both academics and practitioners with a deeper understanding of the challenges in spam review identification and potential avenues for improvement using ML techniques.

The Rise of Fake Reviews: Toward a Marketing-Oriented Framework for Understanding Fake Reviews

Reviews have become an essential part of consumer decision-making. However, the rise in fake reviews can lead consumers astray and provide misleading information to sell more products or harm competing brands. This study systematically analyzes 229 studies on fake reviews using structural topic modeling. The study outlines the antecedents, decisions, and outcomes (ADO) of fake reviews in marketing and summarizes the theories, contexts, and methods (TCM) used by research in this domain. Additionally, the study integrates the sender, message, channel, and receiver (SMCR) communication model with dual-factor theory to propose a comprehensive framework that categorizes marketing factors that contribute to or inhibit the creation and transmission of fake reviews along with their marketing implications. Finally, the study outlines opportunities for further research and provides equivalent recommendations for marketers and marketing researchers to deal with fake reviews.

NetSpam : A Network-Based Spam Detection Framework for Reviews in Online Social Media.

The system ensures about irrelevant or promotional comments on blogs, forums, or social media. In today’s digital age, online reviews play a crucial role in shaping consumer decisions and business reputations. However, the open nature of these platforms also provides opportunities for malicious actors to post spam reviews, misleading users and undermining the credibility of genuine feedback. Social media enables customers to share their views, opinions and experiences as product reviews. These product reviews facilitate customers in buying quality products. Due to the significance of online reviews, fake reviews, commonly known as spam reviews are generated to mislead the potential customers in decision-making. To cater this issue, review spam detection has become an active research area.

Automatic detection of fake reviews at marketplaces using expert-based features and consumers’ reactions

The article presents the results of a practical study of the features of fake reviews that are described by marketers and other experts. Due to the abundance of fake reviews on marketplaces, consumer trust falls not only in the seller or platform, but in the genre itself. The paper presents the results of automatic classification of reviews from Russian marketplaces into potentially fake and honest ones using modelling of features that experts call labels of a fake review (presence of template words, exclamation marks, emoji, positive sentiment), and machine learning algorithms. To solve the problem, a corpus of 6 288 texts from the Russian marketplaces Wildberries and Megamarket has been collected. The target variable (predicted class) is the ratio of likes and dislikes given to the review by other buyers. The best result is demonstrated by the support vector machine algorithm in binary classification into reviews with a low and high ratings (without neutral ones). The classification model confirms that the formal features identified by experts as indicating fake reviews indeed have predictive potential. The quality of the model is reduced by the imbalance in classes and insufficient number of reviews with buyer reactions in our corpus, which leaves room for further work.

An Examination of the Existing Literature Concerning Fraudulent Online Reviews: Obstacles and Potential Remedies

In the contemporary digital era, online consumer evaluations exert significant sway over purchase choices, shaping consumer viewpoints and affecting business profitability. Nonetheless, the rise of counterfeit reviews has emerged as a notable apprehension, prompting scholars to investigate various methodologies for identification. This extensive review paper acts as a reservoir of information, consolidating an extensive array of literature dedicated to detecting fake reviews. It meticulously scrutinizes diverse datasets, illuminating the numerous hurdles posed by these misleading entries. Despite progress made in curtailing the impact of counterfeit reviews, this review exposes persisting gaps in our comprehension. Consequently, it calls for steadfast exploration and ingenuity in the realm of fake review detection. As the digital landscape continues to evolve, so too must our approaches to safeguard the authenticity of online consumer input.

Effect of Counterfeits and Fake Reviews in Markets for Credence Goods

Counterfeits are a persistent problem in online marketplaces, in particular regarding credence goods (e.g., nutritional supplements), as their qualities are difficult or impossible to evaluate even after consumption. Concerned about product quality, customers frequently rely on external signals, such as product badges based on ratings. However, even product ratings are not foolproof as unethical sellers may acquire fake positive reviews to exploit product ratings and badge systems. To analyze the impact fake reviews have on credence goods, we consider a two-stage competition between an authentic seller and a deceptive counterfeiter. The market consists of two types of consumers: savvy customers, who understand that endorsement badges are product-dependent and not seller-dependent, and novice customers, who mistakenly believe product badges testify to a seller's authenticity. In the first stage, both sellers simultaneously decide on whether to acquire fake reviews, which partially influences if the product receives an endorsement badge. In the second stage, both sellers simultaneously set their prices and customers make purchasing decisions. Our results indicate that, in equilibrium, the authentic seller does not acquire fake reviews, while the counterfeiter may do so to mislead customers. Moreover, the amount of fake reviews is decreasing in the fraction of savvy consumers, suggesting that online platforms can combat fake reviews by, for instance, clearly highlighting that badges are product-dependent. We also find that having the option to acquire fake reviews may benefit both sellers but always hurts consumers, emphasizing the need for regulation to protect consumers.

Fake It Till You Make It—A Statistical Assessment of the Proportion of Fake Reviews in Closed Reputation Systems

ABSTRACT Fake reviews have become an increasingly relevant issue in electronic commerce. Regulators and platform operators have begun to take action against fake reviews through regulation and litigation. While there has emerged an ample body of research on fake reviews in open reputation systems (i.e. where everyone can write reviews), little is known about the frequency and nature of fake reviews in closed reputation systems (i.e. where the completion of a transaction is a necessary precondition for reviewing). This article assesses the frequency of fake reviews in this particular domain of the platform economy. To do so, we draw on data from Airbnb and investigate systematic differences between first and second rating score distributions, estimating the proportion of potentially fake five-star ratings using KL-divergence. In doing so, we advance the understanding of fake reviews in closed reputation systems, offering theoretical insights into review dynamics and practical strategies for detecting fraudulent behavior using score distributions and user attributes. This shows that early-on reputation is crucial for complementors to overcome the cold-start problem, and platform operators will have to think of ways to allow them to do so (e.g. reputation imports) in order not to jeopardize the overall credibility of their reputation systems.

Mockery, Fake Reviews, and Fake Reviewers

Mockery, Fake Reviews, and Fake Reviewers

E-Commerce Fake Reviews Detection Using LSTM with Word2Vec Embedding

Customer reviews inform potential buyers' decisions, but fake reviews in e-commerce can skew perceptions as customers may feel pressured to leave positive feedback. Detecting fake reviews in e-commerce platforms is a critical challenge, impacting online shopping and deceiving customers. Effective detection strategies, employing deep learning architectures and word embeddings, are essential to combat this issue. Specifically, the study presented in this paper employed a 1-layer Simple LSTM model, a 1D Convolutional model, and a combined CNN+LSTM model. These models were trained using different pre-trained word embeddings including Word2Vec, GloVe, FastText, and Keras embeddings, to convert the text data into vector form. The models were evaluated based on accuracy and F1-score to provide a comprehensive measure of their performance. The results indicated that the Simple LSTM model with Word2Vec embeddings achieved an accuracy of nearly 91% and an F1-score of 0.9024, outperforming all other model-embedding combinations. The 1D convolutional model performed best without any embeddings, suggesting its ability to extract meaningful features from the raw text. The transformer-based models, BERT and DistilBERT, showed progressive learning but struggled with generalization, indicating the need for strategies such as early stopping, dropout, or regularization to prevent overfitting. Notably, the DistilBERT model consistently outperformed the LSTM model, achieving optimal performance with an accuracy of 96% and an F1-score of 0.9639 using a batch size of 32 and a learning rate of 4.00E-05.

Ensemble learning approach for distinguishing human and computer-generated Arabic reviews

While customer reviews are crucial for businesses to maintain their standing in the marketplace, some may employ humans to create favorable reviews for their benefit. However, advances in artificial intelligence have made it less complex to create these reviews, which now rival real ones written by humans. This poses a significant challenge in distinguishing between genuine and artificially generated reviews, thereby impacting consumer trust and decision-making processes. Research has been conducted to classify whether English reviews were authored by humans or computers. However, there is a notable scarcity of similar studies conducted in Arabic. Moreover, the potential of ensemble learning (EL) techniques, such as soft voting, to enhance model performance remains underexplored. This study conducts a comprehensive empirical analysis using various models, including traditional machine learning, deep learning, and transformers, with an investigation into ensemble techniques, like soft voting, to classify human and computer-generated Arabic reviews. Integrating top logistic regression (LR) and convolutional neural network (CNN) models, it achieves an accuracy of 89.70%, akin to AraBERT’s 90.0%. Additionally, a thorough textual analysis, covering parts of speech (POS), emotions, and linguistics reveals significant linguistic disparities between human and computer-generated reviews. Notably, computer-generated reviews exhibit a substantially higher proportion of adjectives (6.3%) compared to human reviews (0.46%), providing crucial insights for discerning between the two review types. The results not only advance natural language processing (NLP) in Arabic but also have significant implications for businesses combating the influence of fake reviews on consumer trust and decision-making.

Review bombing: ideology-driven polarisation in online ratings: The case study of The Last of Us (part II)

AbstractA review bomb is a surge in online reviews, coordinated by a group of people willing to manipulate public opinions. This is a study on a prominent case of review bombing (n = 51,120) of the video game The Last of Us Part II, challenging the assumption that review bombing should be framed solely as misinformation. The impact of fake reviews is substantially small. Ideology-driven ratings associated with a conservative ideology are followed by a grassroots counter-bombing from progressives, aimed at mitigating the effects of the negative ratings. These factions are very similar in other metrics. Preventive measures are proposed.

Effects of AI-Generated Misinformation and Disinformation on the Economy

This study investigates the potential consequences of AI-driven misinformation/disinformation on the economy, which the World Economic Forum has identified as the most significant threat to global stability in the near term. To determine the effects of false and/or fake information on the economy, qualitative research method which involves collecting and analyzing information that is rich in detail, context, and meaning was preferred within the framework of this study, and the following inferences and conclusions were drawn regarding the potential implications and consequences of AI-generated mis/disinformation. Mis/disinformation can severely damage consumer trust, leading to reduced revenue and tarnished reputations for businesses. Fake news and reviews can harm consumers by influencing their decisions and can damage brands, resulting in crisis management efforts and decreased consumer loyalty. In financial markets, dis/misinformation can create divergent opinions among investors, leading to market volatility. Within supply chains, the spread of false information can disrupt operations and have significant economic consequences. AI-driven disinformation can pose additional risks, potentially affecting political stability and economic policies. As AI technology advances, countries are implementing stricter regulations, such as the EU AI Act, which may increase compliance costs, particularly for smaller businesses. This study can be considered important as it aims to contribute to a growing awareness of the complex and multifaceted nature of the impact of AI on the economy by investigating the potential negative effects of AI.

Reviews, trust, and customer experience in online marketplaces: the case of Mercado Libre Colombia

PurposeThe research focuses on the crucial role of online reviews in shaping consumer trust in e-commerce platforms, examining the impact of perceived authentic and fake reviews on purchasing decisions and platform reputation. It assesses how consumers perceive review authenticity and quality and their effects on trust levels in reviews, marketplaces, and reputation systems. It also explores the relationship between trust forms and overall experiences.Design/methodologyA quantitative approach is employed, utilizing a questionnaire distributed to recent Mercado Libre buyers. To test hypotheses, data from 326 valid responses are analyzed using confirmatory factor analysis and Partial Least Squares Structural Equation Modeling (PLS-SEM).FindingsFindings reveal that fake review perception negatively affects trust in rating systems, while high-quality reviews positively influence all trust forms. Customer experience is directly impacted by trust in marketplaces and rating systems, indicating a mediation effect of trust in the rating system on the relationship between fake review perception and customer experience.Research limitations/implicationsLimitations include using a convenience sample and focusing on trust in the rating system rather than reviews or the marketplace, suggesting avenues for future research. Practical implications include recommendations to ensure review quality, enhance rating system controls, and promote review usage in the purchase process.OriginalityThe study addresses a timely and relevant gap in understanding the impact of reviews on e-commerce trust, particularly within the context of Latin America and Mercado Libre’s dominance in the region’s e-commerce landscape.

Deep Learning-Based Approach for Monitoring and Controlling Fake Reviews

In the last decade, E-commerce has developed into the world’s biggest stage for shopping. It has allowed people around the world to directly communicate without any barriers to purchasing the products as per requirements. Internet technologies have reshaped E-commerce since product reviews have become a vital part of online shopping due to their rapid growth. But with widespread usage, it has also brought forth an influx in rates of fake reviews. Fake reviews, which are frequently used to influence public perception, are now a widespread occurrence due to the open nature of E-commerce. Using different learning techniques, many methods and techniques are implemented to spot false reviews and fake behavior. This research aims to use a recurrent neural network (RNN) to combine content and data to identify false product reviews. The proposed approach, which is related to spam indicators, makes use of both product reviews and reviewers' behavioral characteristics. The fine-grained burst pattern analysis is used to conduct a more thorough investigation of produced testimonials during "suspicious" periods in the proposed approach. Additionally, a customer's previous review data are utilized to determine their overall "authorship" reputation, which serves as a barometer for the authenticity of most recent reviews. For the proposed theory, we examined the real-world Amazon review dataset and produced more accurate findings than previous methodologies. In addition to this, our proposed deep learning-based model performance has been validated utilizing the benchmark Yelp Open dataset and IMDB dataset.

Retraction Note: Transparency in healthcare and e-commerce: detecting online fake reviews using a dense neural network model with relevance mapping

Retraction Note: Transparency in healthcare and e-commerce: detecting online fake reviews using a dense neural network model with relevance mapping